Interpretable Machine Learning for Photovoltaic Defect Screening: a Reproducible Electroluminescence Benchmark
Year: 2026
Type: article
Abstract: Electroluminescence (EL) imaging can reveal cracks, inactive regions, and other abnormalities in photovoltaic (PV) cells, but manual interpretation is labor intensive and depends on specialist judgment. In this study, I evaluate transparent image features as a reproducible screening baseline. I use the public ELPV dataset, which contains 2,624 normalized grayscale cell images from 44 modules with expert-assigned defect probabilities, and resize each image to 96 x 96 pixels. I compare three representations: 62 interpretable intensity and gradient features, 324 histogram-of-oriented-gradient features, and their 386-feature combination. Using stratified five-fold cross-validation, I assess class-balanced logistic models for two prespecified outcomes: any annotated defect and high-confidence defect. For any annotated defect, the combined representation achieves mean accuracy 0.721, F1 0.670, receiver-operating-characteristic area under the curve (ROC AUC) 0.778, and precision-recall area under the curve 0.751. For high-confidence defects, the interpretable representation produces the highest mean F1, 0.638, and ROC AUC, 0.816. On the primary outcome, out-of-fold F1 is 0.712 for monocrystalline and 0.639 for polycrystalline cells. I use standardized coefficients to connect predictions with observable intensity dispersion, gradient tails, and dark-pixel fractions. I present this benchmark as an auditable basis for triage and method comparison, not as autonomous disposition, electrical qualification, or field deployment. Module-grouped external validation and correlation with calibrated electrical measurements remain necessary.
Author: Khandoker Hoque
Institution: San Francisco Bay University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203673598